DP-Net: Dynamic Programming Guided Deep Neural Network Compression
Autor: | Yang, Dingcheng, Yu, Wenjian, Zhou, Ao, Mu, Haoyuan, Yao, Gary, Wang, Xiaoyi |
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Rok vydání: | 2020 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantization and an optimization process to train a clustering-friendly DNN. Experiments showed that the DP-Net allows larger compression than the state-of-the-art counterparts while preserving accuracy. The largest 77X compression ratio on Wide ResNet is achieved by combining DP-Net with other compression techniques. Furthermore, the DP-Net is extended for compressing a robust DNN model with negligible accuracy loss. At last, a custom accelerator is designed on FPGA to speed up the inference computation with DP-Net. Comment: 7pages, 4 figures |
Databáze: | arXiv |
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